Summary Stats
agr <- d %>%
group_by(FeatureMatch) %>%
summarize(MeanPropSimilar = mean(Response),
SD = sd(Response))
print(agr)
## # A tibble: 5 × 3
## FeatureMatch MeanPropSimilar SD
## <chr> <dbl> <dbl>
## 1 ConceptualMatchingOnly 10.3 16.9
## 2 MaxMatch 50.9 34.0
## 3 MaxMismatch 9.71 14.5
## 4 SelfPair 98.8 1.7
## 5 ValenceMatchingOnly 39.8 31.4
agr <- d %>%
filter(!FeatureMatch == "SelfPair")
ggplot(agr, aes(Response,fill=FeatureMatch)) +
geom_density(alpha = .5)
# facet_wrap(~FeatureMatch)
ggplot(agr, aes(Response,fill=FeatureMatch)) +
geom_density(alpha = .5) +
facet_wrap(~FeatureMatch)
agr <- d %>%
group_by(FeatureMatch) %>%
reframe(MeanPropSimilar = mean(Response),
CILow = ci.low(Response),
CIHigh = ci.high(Response)) %>%
mutate(YMin = MeanPropSimilar - CILow,
YMax = MeanPropSimilar + CIHigh)
# View(agr)
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=reorder(FeatureMatch,MeanPropSimilar),y=MeanPropSimilar, fill=FeatureMatch)) +
geom_bar(position=dodge,stat="identity") +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9)) +
theme(axis.text.x = element_text(angle = 10, hjust = 1))
agr <- d %>%
group_by(WordPair,FeatureMatch) %>%
reframe(MeanPropSimilar = mean(Response),
CILow = ci.low(Response),
CIHigh = ci.high(Response)) %>%
mutate(YMin = MeanPropSimilar - CILow,
YMax = MeanPropSimilar + CIHigh)
# Set dodge width to match violin grouping
dodge_width <- 0.9
ggplot(agr, aes(x = reorder(FeatureMatch,MeanPropSimilar), y = MeanPropSimilar, fill = FeatureMatch)) +
geom_violin(trim = FALSE, alpha = 0.4, position = position_dodge(width = dodge_width)) +
# Add mean point with matching dodge
stat_summary(fun = mean,
geom = "point",
shape = 23,
size = 3,
position = position_dodge(width = dodge_width)) +
stat_summary(fun = median,
geom = "crossbar",
width = 0.3,
linewidth = 0.2,
position = position_dodge(width = dodge_width)) +
labs(y = "MeanPropSimilar", x = "Experiment") +
theme(text = element_text(family = "Helvetica")) +
# theme(axis.text.x = element_text(angle = 10, hjust = 1))
theme(
legend.position = "top",
legend.direction = "horizontal"
# axis.text.x = element_text(angle = 10, hjust = .5, margin = margin(t = 10)),
# plot.margin = margin(10, 20, 20, 10) # top, right, bottom, left (in pts)
)
by-item
ggplot(d, aes(x = reorder(WordPair,Response), y = Response, fill = FeatureMatch)) +
geom_violin(trim = FALSE, position = position_dodge(width = dodge_width), alpha = 0.4) +
stat_summary(fun = mean, geom = "point", shape = 23, size = 3,
position = position_dodge(width = dodge_width)) +
stat_summary(fun = median, geom = "crossbar", width = 0.3, linewidth = 0.2,
position = position_dodge(width = dodge_width)) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
labs(y = "Response", x = "WordPair")
agr <- d %>%
group_by(WordPair,FeatureMatch) %>%
reframe(MeanPropSimilar = mean(Response),
CILow = ci.low(Response),
CIHigh = ci.high(Response)) %>%
mutate(YMin = MeanPropSimilar - CILow,
YMax = MeanPropSimilar + CIHigh)
ggplot(agr, aes(x = reorder(WordPair, MeanPropSimilar), y = MeanPropSimilar, fill = FeatureMatch)) +
geom_col(position = position_dodge(width = dodge_width), width = 0.7, alpha = 0.8) +
geom_errorbar(aes(ymin = CILow, ymax = CIHigh),
width = 0.2, position = position_dodge(width = dodge_width)) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
labs(y = "Mean Prop Similar", x = "WordPair")
agr <- d %>%
group_by(ID.Ibex, WordPair, FeatureMatch) %>%
summarise(
MeanPropSimilar = mean(Response),
SE = sd(Response) / sqrt(n()),
.groups = "drop"
) %>%
mutate(
CILow = MeanPropSimilar - 1.96 * SE,
CIHigh = MeanPropSimilar + 1.96 * SE
)
ggplot(agr, aes(x = reorder(WordPair,MeanPropSimilar), y = MeanPropSimilar, fill = FeatureMatch)) +
geom_violin(trim = FALSE, position = position_dodge(width = dodge_width), alpha = 0.4) +
stat_summary(fun = mean, geom = "point", shape = 23, size = 3,
position = position_dodge(width = dodge_width)) +
stat_summary(fun = median, geom = "crossbar", width = 0.3, linewidth = 0.2,
position = position_dodge(width = dodge_width)) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
labs(y = "MeanPropSimilar", x = "WordPair")
by-participant
agr <- d %>%
group_by(ID.Ibex,FeatureMatch) %>%
reframe(MeanPropSimilar = mean(Response),
CILow = ci.low(Response),
CIHigh = ci.high(Response)) %>%
mutate(YMin = MeanPropSimilar - CILow,
YMax = MeanPropSimilar + CIHigh)
# View(agr)
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=reorder(ID.Ibex,MeanPropSimilar),y=MeanPropSimilar, fill=FeatureMatch)) +
geom_bar(position=dodge,stat="identity") +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9)) +
facet_wrap(~FeatureMatch,ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
agr <- d %>%
group_by(ID.Ibex,WordPair,FeatureMatch) %>%
reframe(MeanPropSimilar = mean(Response),
CILow = ci.low(Response),
CIHigh = ci.high(Response)) %>%
mutate(YMin = MeanPropSimilar - CILow,
YMax = MeanPropSimilar + CIHigh)
# View(agr)
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=reorder(ID.Ibex,MeanPropSimilar),y=MeanPropSimilar, fill=FeatureMatch)) +
geom_bar(position=dodge,stat="identity") +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9)) +
facet_wrap(~WordPair,ncol=5) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
agr <- d %>%
group_by(ID.Ibex,WordPair,FeatureMatch) %>%
reframe(MeanPropSimilar = mean(Response),
CILow = ci.low(Response),
CIHigh = ci.high(Response)) %>%
mutate(YMin = MeanPropSimilar - CILow,
YMax = MeanPropSimilar + CIHigh)
# Step 1: Filter for FeatureMatch level to use for ordering
order_df <- agr %>%
filter(FeatureMatch == "SelfPair") %>% # or "ValenceMatchingOnly", etc.
group_by(WordPair) %>%
summarise(Avg = mean(MeanPropSimilar)) %>%
arrange(desc(Avg)) # or arrange(Avg) for ascending
# Step 2: Set factor levels of WordPair
agr$WordPair <- factor(agr$WordPair, levels = order_df$WordPair)
# Step 3: Plot
ggplot(data = agr, aes(x = reorder(ID.Ibex, MeanPropSimilar), y = MeanPropSimilar, fill = FeatureMatch)) +
geom_bar(position = dodge, stat = "identity") +
geom_errorbar(aes(ymin = YMin, ymax = YMax), width = 0.25, position = dodge) +
facet_wrap(~WordPair, ncol = 2) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
agr <- d %>%
group_by(ID.Ibex,WordPair,FeatureMatch) %>%
reframe(MeanPropSimilar = mean(Response),
CILow = ci.low(Response),
CIHigh = ci.high(Response)) %>%
mutate(YMin = MeanPropSimilar - CILow,
YMax = MeanPropSimilar + CIHigh)
# Step 1: Filter for FeatureMatch level to use for ordering
order_df <- agr %>%
filter(FeatureMatch == "MaxMismatch") %>% # or "ValenceMatchingOnly", etc.
group_by(WordPair) %>%
summarise(Avg = mean(MeanPropSimilar)) %>%
arrange(desc(Avg)) # or arrange(Avg) for ascending
# Step 2: Set factor levels of WordPair
agr$WordPair <- factor(agr$WordPair, levels = order_df$WordPair)
# Step 3: Plot
ggplot(data = agr, aes(x = reorder(ID.Ibex, MeanPropSimilar), y = MeanPropSimilar, fill = FeatureMatch)) +
geom_bar(position = dodge, stat = "identity") +
geom_errorbar(aes(ymin = YMin, ymax = YMax), width = 0.25, position = dodge) +
facet_wrap(~WordPair, ncol = 2) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
agr <- d %>%
group_by(ID.Ibex,WordPair,FeatureMatch) %>%
reframe(MeanPropSimilar = mean(Response),
CILow = ci.low(Response),
CIHigh = ci.high(Response)) %>%
mutate(YMin = MeanPropSimilar - CILow,
YMax = MeanPropSimilar + CIHigh)
# Step 1: Filter for FeatureMatch level to use for ordering
order_df <- agr %>%
filter(FeatureMatch == "MaxMatch") %>% # or "ValenceMatchingOnly", etc.
group_by(WordPair) %>%
summarise(Avg = mean(MeanPropSimilar)) %>%
arrange(desc(Avg)) # or arrange(Avg) for ascending
# Step 2: Set factor levels of WordPair
agr$WordPair <- factor(agr$WordPair, levels = order_df$WordPair)
# Step 3: Plot
ggplot(data = agr, aes(x = reorder(ID.Ibex, MeanPropSimilar), y = MeanPropSimilar, fill = FeatureMatch)) +
geom_bar(position = dodge, stat = "identity") +
geom_errorbar(aes(ymin = YMin, ymax = YMax), width = 0.25, position = dodge) +
facet_wrap(~WordPair, ncol = 2) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
agr <- d %>%
group_by(ID.Ibex,WordPair,FeatureMatch) %>%
reframe(MeanPropSimilar = mean(Response),
CILow = ci.low(Response),
CIHigh = ci.high(Response)) %>%
mutate(YMin = MeanPropSimilar - CILow,
YMax = MeanPropSimilar + CIHigh)
# Step 1: Filter for FeatureMatch level to use for ordering
order_df <- agr %>%
filter(FeatureMatch == "ConceptualMatchingOnly") %>% # or "ValenceMatchingOnly", etc.
group_by(WordPair) %>%
summarise(Avg = mean(MeanPropSimilar)) %>%
arrange(desc(Avg)) # or arrange(Avg) for ascending
# Step 2: Set factor levels of WordPair
agr$WordPair <- factor(agr$WordPair, levels = order_df$WordPair)
# Step 3: Plot
ggplot(data = agr, aes(x = reorder(ID.Ibex, MeanPropSimilar), y = MeanPropSimilar, fill = FeatureMatch)) +
geom_bar(position = dodge, stat = "identity") +
geom_errorbar(aes(ymin = YMin, ymax = YMax), width = 0.25, position = dodge) +
facet_wrap(~WordPair, ncol = 2) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
agr <- d %>%
group_by(ID.Ibex,WordPair,FeatureMatch) %>%
reframe(MeanPropSimilar = mean(Response),
CILow = ci.low(Response),
CIHigh = ci.high(Response)) %>%
mutate(YMin = MeanPropSimilar - CILow,
YMax = MeanPropSimilar + CIHigh)
# Step 1: Filter for FeatureMatch level to use for ordering
order_df <- agr %>%
filter(FeatureMatch == "ValenceMatchingOnly") %>% # or "ValenceMatchingOnly", etc.
group_by(WordPair) %>%
summarise(Avg = mean(MeanPropSimilar)) %>%
arrange(desc(Avg)) # or arrange(Avg) for ascending
# Step 2: Set factor levels of WordPair
agr$WordPair <- factor(agr$WordPair, levels = order_df$WordPair)
# Step 3: Plot
ggplot(data = agr, aes(x = reorder(ID.Ibex, MeanPropSimilar), y = MeanPropSimilar, fill = FeatureMatch)) +
geom_bar(position = dodge, stat = "identity") +
geom_errorbar(aes(ymin = YMin, ymax = YMax), width = 0.25, position = dodge) +
facet_wrap(~WordPair, ncol = 2) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
